

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case				:      Any   		=			16
__snake_case				:      int   		=			32


def 					_UpperCAmelCase				(						a__  ,	a__ = 1_6):

			'''simple docstring'''



			a_						:						List[Any] 							= AutoTokenizer.from_pretrained("""bert-base-cased""")
			a_						:						int 							= load_dataset("""glue"""  ,	"""mrpc""")

			def tokenize_function(a__):
						# max_length=None => use the model max length (it's actually the default)
						a_						:						Optional[int] 							= tokenizer(examples["""sentence1"""]  ,	examples["""sentence2"""]  ,	truncation=a__  ,	max_length=a__)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						a_						:						Dict 							= datasets.map(
						    a__  ,	batched=a__  ,	remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,	)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			a_						:						Optional[Any] 							= tokenized_datasets.rename_column("""label"""  ,	"""labels""")

			def collate_fn(a__):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						a_						:						Tuple 							= 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									a_						:						List[Any] 							= 1_6
						elif accelerator.mixed_precision != "no":
									a_						:						Dict 							= 8
						else:
									a_						:						List[Any] 							= None

						return tokenizer.pad(
						    a__  ,	padding="""longest"""  ,	max_length=a__  ,	pad_to_multiple_of=a__  ,	return_tensors="""pt"""  ,	)

			# Instantiate dataloaders.
			a_						:						Tuple 							= DataLoader(
			    tokenized_datasets["""train"""]  ,	shuffle=a__  ,	collate_fn=a__  ,	batch_size=a__)
			a_						:						Optional[int] 							= DataLoader(
			    tokenized_datasets["""validation"""]  ,	shuffle=a__  ,	collate_fn=a__  ,	batch_size=a__)

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	__snake_case				:      int   		=			mocked_dataloaders  # noqa: F811


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,	a__) == "1":
						a_						:						Optional[Any] 							= 2
			# New Code #
			a_						:						List[Any] 							= int(args.gradient_accumulation_steps)
			a_						:						List[str] 							= int(args.local_sgd_steps)
			# Initialize accelerator
			a_						:						Any 							= Accelerator(
			    cpu=args.cpu  ,	mixed_precision=args.mixed_precision  ,	gradient_accumulation_steps=a__)
			if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
						raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""")
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a_						:						Optional[Any] 							= config["""lr"""]
			a_						:						Any 							= int(config["""num_epochs"""])
			a_						:						List[Any] 							= int(config["""seed"""])
			a_						:						Union[str, Any] 							= int(config["""batch_size"""])

			a_						:						Any 							= evaluate.load("""glue"""  ,	"""mrpc""")

			set_seed(a__)
			a_							,					a_						:						List[Any] 							= get_dataloaders(a__  ,	a__)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a_						:						int 							= AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""  ,	return_dict=a__)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			a_						:						Optional[int] 							= model.to(accelerator.device)

			# Instantiate optimizer
			a_						:						str 							= AdamW(params=model.parameters()  ,	lr=a__)

			# Instantiate scheduler
			a_						:						Optional[int] 							= get_linear_schedule_with_warmup(
			    optimizer=a__  ,	num_warmup_steps=1_0_0  ,	num_training_steps=(len(a__) * num_epochs)  ,	)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			a_							,					a_							,					a_							,					a_							,					a_						:						Dict 							= accelerator.prepare(
			    a__  ,	a__  ,	a__  ,	a__  ,	a__)

			# Now we train the model
			for epoch in range(a__):
						model.train()
						with LocalSGD(
						    accelerator=a__  ,	model=a__  ,	local_sgd_steps=a__  ,	enabled=local_sgd_steps is not None) as local_sgd:
									for step, batch in enumerate(a__):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device)
												# New code #
												# We use the new `accumulate` context manager to perform gradient accumulation
												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
												with accelerator.accumulate(a__):
															a_						:						Optional[int] 							= model(**a__)
															a_						:						Optional[int] 							= output.loss
															accelerator.backward(a__)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

						model.eval()
						for step, batch in enumerate(a__):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									with torch.no_grad():
												a_						:						Dict 							= model(**a__)
									a_						:						Optional[int] 							= outputs.logits.argmax(dim=-1)
									a_							,					a_						:						int 							= accelerator.gather_for_metrics((predictions, batch["""labels"""]))
									metric.add_batch(
									    predictions=a__  ,	references=a__  ,	)

						a_						:						Optional[int] 							= metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''  ,	a__)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Any 							= argparse.ArgumentParser(description="""Simple example of training script.""")
			parser.add_argument(
			    """--mixed_precision"""  ,	type=a__  ,	default=a__  ,	choices=["""no""", """fp16""", """bf16""", """fp8"""]  ,	help="""Whether to use mixed precision. Choose"""
			    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
			    """and an Nvidia Ampere GPU."""  ,	)
			# New Code #
			parser.add_argument(
			    """--gradient_accumulation_steps"""  ,	type=a__  ,	default=1  ,	help="""The number of minibatches to be ran before gradients are accumulated."""  ,	)
			parser.add_argument(
			    """--local_sgd_steps"""  ,	type=a__  ,	default=8  ,	help="""Number of local SGD steps or None to disable local SGD""")
			parser.add_argument("""--cpu"""  ,	action="""store_true"""  ,	help="""If passed, will train on the CPU.""")
			a_						:						Dict 							= parser.parse_args()
			a_						:						Union[str, Any] 							= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
			training_function(a__  ,	a__)


if __name__ == "__main__":
	main()





from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= SMALL_MODEL_IDENTIFIER
									a_						:						Optional[int] 							= """pt"""
									a_						:						Union[str, Any] 							= """tf"""





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> str:
									a_						:						Union[str, Any] 							= AutoModel.from_pretrained(self.test_model    )
									model_pt.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[str]:
									a_						:						int 							= TFAutoModel.from_pretrained(self.test_model  ,      from_pt=_lowercase    )
									model_tf.save_pretrained(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						str 							= """mock_framework"""

									# Framework provided - return whatever the user provides
									a_						:						Optional[int] 							= FeaturesManager.determine_framework(self.test_model  ,      _lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )

									# Local checkpoint and framework provided - return provided framework
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Optional[int] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						List[str] 							= FeaturesManager.determine_framework(_lowercase  ,      _lowercase    )
												self.assertEqual(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									# PyTorch checkpoint
									with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# TensorFlow checkpoint
									with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(_lowercase    )
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Invalid local checkpoint
									with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(_lowercase    ):
															a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						int 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# PyTorch not in environment -> use TensorFlow
									a_						:						Dict 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Union[str, Any] 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_tf    )

									# Both in environment -> use PyTorch
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									a_						:						List[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												a_						:						Any 							= FeaturesManager.determine_framework(self.test_model    )
												self.assertEqual(_lowercase  ,      self.framework_pt    )

									# Both not in environment -> raise error
									a_						:						List[str] 							= MagicMock(return_value=_lowercase    )
									a_						:						Optional[Any] 							= MagicMock(return_value=_lowercase    )
									with patch("""transformers.onnx.features.is_tf_available"""  ,      _lowercase    ), patch(
									    """transformers.onnx.features.is_torch_available"""  ,      _lowercase    ):
												with self.assertRaises(_lowercase    ):
															a_						:						Dict 							= FeaturesManager.determine_framework(self.test_model    )


